Fairness Testing: A Comprehensive Survey and Analysis of Trends

• URL: http://arxiv.org/abs/2207.10223v4
• Date: Wed, 6 Mar 2024 14:07:48 GMT
• Title: Fairness Testing: A Comprehensive Survey and Analysis of Trends
• Authors: Zhenpeng Chen, Jie M. Zhang, Max Hort, Mark Harman, Federica Sarro
• Abstract summary: Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. This paper offers a comprehensive survey of existing studies in this field.
• Score: 30.637712832450525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing.

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